Detection of Diverse Sequence Types of Legionella pneumophila by Legiolert Enzymatic-Based Assay and the Development of a Long-Term Storage Protocol

ABSTRACT Legiolert is a rapid culture-based enzymatic method for the detection and quantification of Legionella pneumophila in potable and nonpotable water samples. We aimed to assess the ability of this assay to detect diverse sequence types and validated a simple method to preserve samples. We used this assay on 253 potable and 165 nonpotable cooling tower water samples from various buildings in Québec, Canada, and performed sequence-based typing on 96 isolates. Six sequence types were identified, including ST1, ST378, ST1427, ST2859, ST3054, and ST3069. Whole-genome sequencing revealed that ST2859 was a member of the L. pneumophila subspecies fraseri. Additional tests with pure isolates also found that subspecies Pascullei and Raphaeli could be detected via Legiolert. Eight storage methods, including the current recommendation to store Legiolert trays at 4°C, were evaluated for their ability to preserve viable cultures. Of those, storage of Legiolert culture with 10% glycerol at −80°C produced the best results, fully preserving culturable Legionella for at least 12.5 months. We incorporated these findings into a standard procedure for processing Legiolert packets. Overall, Legiolert captures a variety of common and new STs in addition to important L. pneumophila subspecies and can be easily stored, which allows the conservation of a population of isolates for later characterization. IMPORTANCE Legionnaires’ disease is caused by the bacterium Legionella pneumophila, which can be found in a variety of water systems. When outbreaks of Legionnaires’ disease occur, it is necessary to find the water systems transmitting the bacterium to humans. Access to historical isolates from water system samples is key for success in identifying sources but current regulations and isolation protocols mean very few isolates are obtained and stored long-term. We showed here that the Legiolert test could detect and produce isolates of a variety of L. pneumophila subspecies and types. In addition, the Legiolert test medium containing a representative population of isolates could be preserved for at least 12 months at −80°C with the addition of glycerol to the test medium. Therefore, we confirmed that the Legiolert method could be a useful tool for retrospective analysis of potential sources for an outbreak.


Recipes for media and agar
Buffered-charcoal yeast extract (BCYE) agar -Add 10 g of yeast extract, 10 g of ACES buffer, and 1g of α-ketoglutaric acid potassium salt to 950 mL of de-ionized water. Then adjust the pH to 6.90 +/-0.05 using 10M KOH. Add 2 g of charcoal and 15 g of agar and autoclave at 121ºC. Cool to 55ºC and aseptically add 10 mL of 4% (w/v) L-cysteine, 10 mL of 2.5% (w/v) of iron pyrophosphate and any other additional supplements before pouring plates.
Charcoal yeast extract (CYE) agar -Add 10 g of yeast extract and 10 g of ACES buffer to 950 mL of de-ionized water. Then adjust the pH to 6.90 +/-0.05 using 10M KOH. Add 2 g of charcoal and 15 g of agar and autoclave at 121ºC. Cool to 55ºC and aseptically add 10 mL of 4% (w/v) L-cysteine, 10 mL of 2.5% (w/v) of iron pyrophosphate and any other additional supplements before pouring plates.
ACES buffered yeast extract (AYE) liquid -Add 10 g of yeast extract and 10 g of ACES buffer to 950 mL of de-ionized water. Then adjust the pH to 6.90 +/-0.05 using 10M KOH. Autoclave at 121ºC. Cool to 55ºC and aseptically add 10 mL of 4% (w/v) L-cysteine, 10 mL of 2.5% (w/v) of iron pyrophosphate and any other additional supplements before pouring plates.

Sampling model using binomial distribution
The binomial distribution formula can be used to calculate the probability of 'x' outcomes in 'n' independent trials, given the probability 'p' of 'x' for each trial as follows: Where P(x) is the binomial probability, x is the number of times for a specific outcome, n in the number of trials, p is the probability of success in a single trial and q, the probability of failure. In applying it to sampling Legionella colonies, the probability of detecting any ST type is the same as its frequency in the population. Given the large population size, we can assume frequency will remain constant between sampling trials. Hence this scenario fulfills the assumptions for a binomial distribution. While there can be more than two STs within a population, we are only concerned with the probability of detecting the non-dominant strain. Therefore, ST can be split into two categories, dominant and non-dominant, and the probability of detecting a non-dominant strain at least once in a set of trials (

Well sampling model using hypergeometric distribution
The hypergeometric distribution can be used to calculate the probability of obtaining 'k' outcomes in 'n' trials given that we are randomly sampling, without replacement, from a population size 'N' as follows: Equation S3: Where K is the number of successes in the population, k is the number of observed successes, N is the population size, n is the number of draws, inserted in the combination formula: Here it can be applied to determine the chance of selecting a certain number of wells with the minor strain given that 1) samples were diluted enough that a single bacteria inoculated each small well 2) the proportion of wells containing the minor strain is the same as its frequency in the sample 3) all 96 small wells are positive. Using a minor strain frequency of 0.2 as an example we can generate the following set of probabilities for different composite sizes in the table below: Probability of selecting 'k' wells with the minor strain in composite Each possibility above results in a different proportion of the minor strain in the final composite and will affect the probability of sampling the minor strain from that final composite as in equation S2. Assuming 5 colonies will be screened from the composite and applying equation S2, the following probabilities are calculated for each case:

Figure S2
-Legiolert trays with atypical results due to interfering flora. Interfering flora can produce obviously abnormal well appearance such as black wells or floating mold (centre and right). Or it is discovered when non-Legionella colony morphology appears on GVPC (centre and right) plate or failure of subsequent cysteine test (left).